{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "OoDSDA7fBPHk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "59f5ba6e-1777-4cc1-a921-b6aa65612909"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |██████████████████████████████▎ | 834.1 MB 1.4 MB/s eta 0:00:34tcmalloc: large alloc 1147494400 bytes == 0x21ba000 @  0x7fec42edf615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
            "\u001b[K     |████████████████████████████████| 881.9 MB 20 kB/s \n",
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            "\u001b[K     |████████████████████████████████| 271 kB 50.3 MB/s \n",
            "\u001b[?25h  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 5.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 135 kB 33.9 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 376 kB 45.7 MB/s \n",
            "\u001b[?25h  Building wheel for pytorchvideo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Collecting Pillow==9.0.0\n",
            "  Downloading Pillow-9.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3 MB 5.3 MB/s \n",
            "\u001b[?25hInstalling collected packages: Pillow\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 9.1.0\n",
            "    Uninstalling Pillow-9.1.0:\n",
            "      Successfully uninstalled Pillow-9.1.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Pillow-9.0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install torch==1.10.0 torchvision==0.11.1 torchtext==0.11.0 torchaudio==0.10.0 --quiet\n",
        "!pip install pytorch-lightning==1.5.10 --quiet\n",
        "!pip install lightning-flash==0.7.1 --quiet\n",
        "!pip install 'lightning-flash[audio,image, video, text]' --quiet\n",
        "!pip install Pillow==9.0.0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Sux44yzMBYX1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import torch\n",
        "import flash\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from flash import Trainer\n",
        "from flash.audio import SpeechRecognitionData, SpeechRecognition"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"pandas version:\",pd.__version__)\n",
        "print(\"torch version:\",torch.__version__)\n",
        "print(\"flash version:\",flash.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awKbpU85cOVI",
        "outputId": "90282326-a2d7-4a74-ab75-d8af7c8e06bb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pandas version: 1.3.5\n",
            "torch version: 1.10.0+cu102\n",
            "flash version: 0.7.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5O49SA8kBaMI",
        "outputId": "65f4ed49-afd5-4d3e-bcd2-d8f42bd1c409"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Please follow instructions in the book to get dataset and put into your google drive."
      ],
      "metadata": {
        "id": "Fzrg_ScR-m00"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lew9SYSpBbt4"
      },
      "outputs": [],
      "source": [
        "!unzip '/content/gdrive/My Drive/Colab Notebooks/scottish_english_female.zip'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "OpruBl_GCmcK",
        "outputId": "6d54a4c8-740c-4166-fd9c-4730a44a808e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(54, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    not_required            speech_files  \\\n",
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              "\n",
              "                                               targets  \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-5ec6462c-9418-44fa-8c80-dfb4adbf51e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "random.seed(10)\n",
        "df_scottish = pd.read_csv(\"line_index.csv\", header=None, names=['not_required', 'speech_files', 'targets'])\n",
        "df_scottish = df_scottish.sample(frac=0.06)\n",
        "print(df_scottish.shape)\n",
        "df_scottish.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "63vxmeWJDNcs",
        "outputId": "87f61156-a1f1-441d-bfb9-adfa05754f40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  speech_files  \\\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cbc3e3a-d3ec-4cf1-b6a7-bdd599df89b0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9cbc3e3a-d3ec-4cf1-b6a7-bdd599df89b0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cbc3e3a-d3ec-4cf1-b6a7-bdd599df89b0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df_scottish = df_scottish[['speech_files', 'targets']]\n",
        "df_scottish['speech_files'] = df_scottish['speech_files'].str.lstrip()\n",
        "\n",
        "df_scottish['speech_files'] = df_scottish['speech_files'].astype(str) + '.wav'\n",
        "df_scottish.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wdCLmUzoDe9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e173fd8c-17ba-4b8a-a028-350c0da9bd14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "310    scf_03397_01072622319.wav\n",
              "223    scf_07049_01133867565.wav\n",
              "868    scf_04310_00395537459.wav\n",
              "835    scf_04310_02050219481.wav\n",
              "620    scf_07049_00239785467.wav\n",
              "Name: speech_files, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "random.seed(10)\n",
        "train_scottish, test_scottish_raw = train_test_split(df_scottish, test_size=0.2)\n",
        "test_scottish = test_scottish_raw['speech_files']\n",
        "test_scottish.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ynQ4fCblEMMz"
      },
      "outputs": [],
      "source": [
        "train_scottish.to_csv('train_scottish.csv')\n",
        "test_scottish.to_csv('test_scottish.csv')"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "QDz_Mx34EZzv",
        "outputId": "b4dbe925-347a-4782-a1f7-fa7b58ee8df4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-15b9ea74ae447d33\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-15b9ea74ae447d33/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac4af6217572433c8f55c4f2e4a0cd44"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b29a7667172c49e0a8ef9dbc116d2a54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-15b9ea74ae447d33/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1452e696c8ef493a8c35c70991311f48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-bfc3565a6d97d227\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-bfc3565a6d97d227/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55881324a70d4030acedfca280721e8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9544f836dcaa46cdb92cee1c741169db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-bfc3565a6d97d227/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9123bac56ce433c906d05d98f190ac8"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "datamodule = SpeechRecognitionData.from_csv(\n",
        "     \"speech_files\",\n",
        "     \"targets\",\n",
        "     train_file=\"train_scottish.csv\",\n",
        "     predict_file=\"test_scottish.csv\",\n",
        "     batch_size=10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SpeechRecognition.available_backbones()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gNnLknWuNJY",
        "outputId": "e57af7ef-7e60-4eea-8dbb-edd57d7b9c8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Anything available from: Hugging Face/transformers (https://github.com/huggingface/transformers)',\n",
              " 'facebook/wav2vec2-base-960h',\n",
              " 'facebook/wav2vec2-large-960h-lv60']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TATaP8JGnfo",
        "outputId": "9e88c4be-757b-4131-8c16-cb87c1bd35a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using 'facebook/wav2vec2-base-960h' provided by Hugging Face/transformers (https://github.com/huggingface/transformers) and PyTorch/fairseq (https://github.com/pytorch/fairseq).\n",
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = SpeechRecognition(backbone=\"facebook/wav2vec2-base-960h\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.get_backbone_details(\"facebook/wav2vec2-base-960h\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2u5itCT_tvMB",
        "outputId": "1d239971-27a6-437b-fcd5-c9be27d4de34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('model_args', <Parameter \"*model_args\">), ('kwargs', <Parameter \"**kwargs\">)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EX6hgPGlEfyG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362,
          "referenced_widgets": [
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            "3f81582ccacb498baea18262ffcb24a3",
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        },
        "outputId": "2592844c-f8fb-49e0-9dcf-97b7c86bb47a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using 16bit native Automatic Mixed Precision (AMP)\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name          | Type           | Params\n",
            "-------------------------------------------------\n",
            "0 | model         | Wav2Vec2ForCTC | 94.4 M\n",
            "1 | train_metrics | ModuleDict     | 0     \n",
            "2 | val_metrics   | ModuleDict     | 0     \n",
            "3 | test_metrics  | ModuleDict     | 0     \n",
            "-------------------------------------------------\n",
            "94.4 M    Trainable params\n",
            "0         Non-trainable params\n",
            "94.4 M    Total params\n",
            "188.793   Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "109229e6c1094df58fc62158cf522803"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer = Trainer(max_epochs=4, gpus=-1, precision=16)\n",
        "trainer.finetune(model, datamodule=datamodule, strategy=\"no_freeze\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "foLCihREFzGs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270,
          "referenced_widgets": [
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        "outputId": "1cd01c59-dce6-48cd-e44f-a854a66ca8a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 4it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48b814f216994d91adf5f97bbcd86397"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['CREDITCARD COMPANIES PURCHASE CHARGED OFF DEBTS IDN ADD THEM TO THE BALANCE OF ABAT AND SWITCH CREDIT CARDS',\n",
              "  'THE JAR CAN ONLY HOLD UP TO TANE MARBLES',\n",
              "  'SMALL SQUARE IMAGE IS REQUIRED',\n",
              "  'JUST TOUCH AND HOLD THE RIGHT EAR BUD AND START TALKING',\n",
              "  'THE POWERS OF THE APPOINTED CHEER ARE LIMITED SO THAT THE CHEER CANNOT ADJOURN A MEETING AT ANY POINT WITHOUT THE MAJORITY VOTE',\n",
              "  'THE SOLEMNITY OF THE OCCASION REQUIRED ATTENTION AND RESPECT',\n",
              "  \"VOICE MATCH HELPS THE ASSISTANT IDENTIFY HEATHER'S VOICE AND TELL HER APART FROM OTHERS BY CREATING A UNIQUE MODEL OF HER VOICE ON THIS DEVICE\",\n",
              "  \"THE GIGGLE ASSISTANT CAN WORK WITH GUGGL PARTNERS TO HELP YE GET THINGS DONE WHETHER IT'S BANG MOVY TICKETS OR GETING YOUR SINK REPAIRED\",\n",
              "  'ON CERTAIN OPERATING SYSTEMS FOWLS MUST BE MOVED OUT OF THE TRASH BEFORE THEY CAN BE ACCESSED AGAIN',\n",
              "  'CONSUMER CONFUSION IS A STATE OF MIND THAT LEADS TO CONSUMERS MEEKING IMPERFECT PURCHASING DECISIONS'],\n",
              " ['CONCENTRATED SOILER POWER USES MOLTEN SAL ENERGY STORAGE IN A TOWER OR IN TROUGH CONFIGURATIONS']]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "trainer.predict(model, datamodule=datamodule)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_scottish.head()"
      ],
      "metadata": {
        "id": "5YG9Ru3Egjt2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "918caa7e-71b3-4ac4-96f1-a460f4fe2330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  speech_files  \\\n",
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              "      <td>scf_05223_01725020876.wav</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1cYaMwrmDgKa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
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            "5250ed19bf35463ca1dae6115123ecb2",
            "06832212a601490f9df61f17576d2d87"
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        },
        "outputId": "01717820-c141-48d1-d7af-38c908ae1044"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 4it [00:00, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['HARRY THEREAFTER RAISED SALARIES FORTUDU EMPLOYES']]\n"
          ]
        }
      ],
      "source": [
        "datamodule = SpeechRecognitionData.from_files(predict_files='scf_02484_00097285334.wav', batch_size=10)\n",
        "\n",
        "predictions = trainer.predict(model, datamodule=datamodule)\n",
        "print(predictions)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_scottish_raw.head()"
      ],
      "metadata": {
        "id": "2I8lh1rJ4MDU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  speech_files  \\\n",
              "310  scf_03397_01072622319.wav   \n",
              "223  scf_07049_01133867565.wav   \n",
              "868  scf_04310_00395537459.wav   \n",
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              "\n",
              "                                               targets  \n",
              "310   Credit card companies purchase charged-off de...  \n",
              "223             The jar can only hold up to 10 marbles  \n",
              "868                     Small square image is required  \n",
              "835   Just touch and hold the right earbud and star...  \n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-d9d8722a-3965-4e64-b4f4-bdb33a89300c button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-d9d8722a-3965-4e64-b4f4-bdb33a89300c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datamodule = SpeechRecognitionData.from_files(predict_files='scf_02484_01925002707.wav', batch_size=10) # Scottish\n",
        "\n",
        "predictions = trainer.predict(model, datamodule=datamodule)\n",
        "print(predictions)"
      ],
      "metadata": {
        "id": "HJn4HWfugl9G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
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        "outputId": "4506b29a-b46b-47e7-d021-ad7a348e6bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 4it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['ASK HER TO CAREFULLY BRING THESE THINGS WITH HER FROM THE STORE']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zK-x0FbX4KdF"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
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      "collapsed_sections": [],
      "background_execution": "on"
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    "kernelspec": {
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